{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rng = np.random.RandomState(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_steps = 1000\n",
      "time = np.arange(n_steps)\n",
      "\n",
      "data_deterministic = (\n",
      "    np.sin(time / 10.)\n",
      "    + 0.7 * np.sin(time / 12. + 5) * np.sin(time / 5.))\n",
      "\n",
      "data_noisy = (\n",
      "    data_deterministic\n",
      "    + 0.2 * np.cumsum(rng.normal(scale=0.3, size=n_steps)))\n",
      "\n",
      "plt.plot(data_deterministic)\n",
      "plt.plot(data_noisy)\n",
      "plt.ylim(-4, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(-4, 4)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KHeR7e4nIZ9efKVboj1uPfsI3AVN5A+Ty9E2orSU3kat8qZg4ff+YHcqYERWK\ndHGJ1ppq+BPChD47Pp9MAh99BJxwQu5nmY73RXz43t++l7ddT9gDZUJY6CboFpZ14w65UQF9zvQd\nIItdK2pPwJUNd4o+ucvqtaIs0JRj8JoKFSZdwk6+ykf25jMp4vTRKAmPGI3ArZtvxaYDm1BbS2LO\nxR7Xlt3vh5yHkEwmRRt8oek7IN6j54rPA0TkvvnN4g/15grdSOnRS93vAPfgVIrQ89n7TMTodToy\ng4hJc96SYGZF6G1+Gypi5ozRXS7n9xzExOn7x0ehimfWqelsqENQIeyJyo7P79hBRCRf6ObDGz7E\nzomdeWvyeMIelMWEhW78TmEevSvkQnnCwDtLuOUWYOumWtz97t0YdglPWh7zjCHpacwxeH2VCg5P\naR49O8WOQQqDn5o6nFaajOLBjx/EQ588BKWSGNJ0EdWVvV5y9NsFF5CfPx//HJ0PdeJT66eiDV6I\nRy92Cs8n9ADw3e+SEsDF5NNzhW5m0qOfKaEvdl0hmQSeew64+OLc93Q60pdCw2KJBKm3lB1eY8M4\nu8VURC2FWRH6u9+9Gwl/dU66Il/MzlhlFOzRH5qywCDLbPjEjvmIVdgFHYXH3iwViRCjueOO3Cky\nkA7dmDVmrDSvxKdW/pPIPWEP5FFhQu9xViCeiBeMrWcfOpLN5ZcDBjVJ2t9jF56KMeQeQnSyLSPu\nCZC2nL7ShN7lIhkd7IVJKQyeMfbKeyoBIHVSWbHhmx//mBwK0txM1m3W/HENAOD7m74PjS4uSpQL\nxWkB8VN4roVYhoULgX/4B3IuqVikFM1sZkLok0lyzdnParEe/WOPkf666KLc9xQKsnvdJ3B7it9P\ndtDnW5sBjkz4Ztby6Lsi12d0OsDf8cZKo+AY/Yh7FLXlmUJvNJRB5liEXWPk1OA73rqD98hCdujm\n0UeJp/Xd73L/LvYUfn71fAw6B3mvyxvxAuHCQq/XA16PTFBVTHfIDVmEX+hlMuDKK8gI9c7QO/l/\nMYuB6QH4LZ05dbertSq4AqWFbrKn74A0sVpGoBJJEqcZdpMZTDEGv2MH8Le/AQ8+SH7eN7kP86vn\nY9uN2/Cp9VM4sF9yj16nI7MIod5iduXKbO64gxxyIXbzFPtwHQYp0ivZZS8YpFiM9XpJLD17E2Ix\nA7zHA9x+O/Dkk5nppWzEhO2EhOyA9ExhNpkVoW/Tt2FqTMdp8Fwdb6g0CA7dTPjHUK/OLDcokwGV\nkRb0Hn5ngeiHAAAgAElEQVTq73v/Prx+gPtIH/ZmqYceAu66i9ubBzI7XV+px/UvX897XZ6wB4mg\nVpDBezzCKli6w24gzC/0APCT865G9da/4G/73sz/i1kMTA8gONaRsWkGAFpNZrhjpbl22V4dIK1H\nv755PT644QP4Ij4EooGiMm/+4z/Iwc3MrGPcN442fRvWNa/DyvqVkFf4JRf6igrynAmtU5MvdAOQ\nLJ+zzxZ/+DTXln2jkcSRS6mhw1UkTMp+z6aYfn/6aeD007nDtAxihF7ITI5p85gUerPGLMrgdRXC\nt967IlNo1Of2vDbRgv22UUTjpAxyKMb91DIe/fh4+tQaPtidrlaSojB8MwVP2IN4sLBHz8TrhAp9\nMmDgncIDQLlCiS/VnQm7r3AJCG/Yi0c/fxQWjwV15e05aYVLGtvgLRsq2E4+uDx6KTw7xuCng9Oo\nrqpGi64FI+6Rojy7t9/OjNFOBaZSJ45pyjVIKHwIhYQvoAn17MQYfCGhB8hZxDt2CGuPYXw8V+hl\nstLDN/nsvZSCqFxhG6C4mdw773DH5tmIWZ8RMsADx7BHz9ShFyr0ok5xik2h2VST83otlmKn7TOM\neckKlYXnBCZnyAlDhQF795KRPVvs2LCF/p6z7sGahjV4ePvD+N223+VeV9iDmE9X0DgZYxdS594d\nciPmz+/RA8CqhSa4o1O8u3dDsRDeGXwHrx94HTe+ciP0yho01OUeibOitQ2RqhHRB2SwmWmPnhH6\nNkMbhl3Dog3e4yELu/Pnp19zBBwwqcj0Rq1UIxD1izLOmTD4fDF6hqVLydGUQonHSU46n4dcitBz\nDfBaLbGvUnbH5vPoxV7v3r3AqlX5PyNm05SYfj8mF2PNarOoWK2uQie4EmMAU2irzRX6xcqvYIfr\nTVz34nUAwFsawBVywVhlRF8fsHhx/t/FHt2VZUq0G9px19a78KO//wjbx7ZnfNYT9iDiLezRa7XC\nQzeukCvn0BEuFrSrIEuW8bb3f73/h7MePwtXPH8FAGBh1SmcFffMumrIqlxwFH+eSYbQM9cjldDX\n1CRTobdWfSuG3cOiDb6/nyxmsgd4R9ABU9VhoS9Xwx/1H3GDLxSjB8TXfHc4SJvl5bnvlZpiyTXA\nA6X3vVQefSxGagQtWJD/c2JDN1LP5KRi1jz67IUZII9HX8Hv0f9t/98QjpEaqolEEqHyMSxpze35\nTtM8xBIxvDfyHl64/AU8uftJznAQIxRChJ4xdsZRZhaMN6zagJ0TOzM+6414EfIUjtGXl5OFoKoy\nYaGbkLuw0Le1AcpgC2+9n122dJmE3pt7cWXl45xCr6vQARUeSTy7TQc2QXufFslkkuwGjgjPZuBr\nt7rBC5VSBWWZEm36tqJCN3195Jg7Nmyh15Rr4I/4j3isVkjopq2NFOcSGhopdNxhKQuyXI4dUHrY\njq9dsf0+OEiuJbueVTYzIfTHbOhGlTCjsjL3QeXrdL4wxn7Hflz0zEV4+ounAQDbB/sBJHFCe+6w\n3NoiQ4fvWgBAd3s3AJIbnQ0To+/ry90CnU1lZeYC2nUrrsM1K65Bq74Vo57M8/E8YQ+CrsIePUDu\nS6WscLiK69ARLtrbAcXomXh3+F3O9yd8E/hS05fw6CWPYkntEkxNVHKedF+lqALKIrBYo4X/CB4G\nBsiJYq8deA0A2VMhRd2ToSHA0EDCNgBSHr3Ycrhc/e4IOFIxerVSDV/Ed8QNXojQazQkG0WoZzs+\nnptxw1CqR3/wIOn3bEr16IeHyfOdTV0duUdCyyAIsXeAZt2IQumflxEDZTCZMk/IGR0lU2k+j/7R\nzx+FQq7AF3ZSxWnnIQtUoQWQy3PTZDo7AeMXv8DDFzwMY5URly65FJOB3IVTR8CB6qpqjIxwP0DZ\nsDv+26u+jSe+/gRadC2cQu+fFib0Oh2gFHDKFDl0JP9iLEA8nsB4Gywebouy+Wy4q/subFhNCqEN\nDgLz5uV+TiaTQZnQYXhCeFC1txf47DPy73CYiElbG1KniDFnDbAN3usFPv5Y8K9AIkGelarqtNA3\n65ox5hlDRwcZXIR6tcxZtmymAlOpGD3zLM6U0IsRESGzBDEHfBw6xN3vgHiP3m4H/v739M8HD4LT\n5tkHpcTjwNat4nYyc1UXBUjuelsbeZaFwNXvXIhdjBU6kzvqYvSbN2/G4sWLsWDBAvz617/m/Ize\neSZnLEwuJ4J84ADp7FNPJTVmmlWd2DmxE8Fo5vD8+cTnuHr51amc6f2WKegVufF5gLQ7ut+Im0+8\nGQBQUVaBe967J+dzFo8FTdqmgifDpP4Wjk5q0ZOMDzaesAc+h3ChL0sIEPqQG4Fpfd6qiMDhtDZ5\nHYYmuV0ym98Gszq9DZbP+wKASpkOIzZh7kciAZx5Jsn+CASI0bW0kNDUuHccZ7SdkdpkxvQ7ANx8\nM7B+vfB6LTbb4a3kyenUHghTlQmOoANGI8mzFnrwNpdXyw7d1KvrMe4bF2XwM5V1U2iAB0jlTaFC\nz8y2uBDr0X//+8B55wE7d5JBlk/oOzvJewDwhz8A3d3A448L/z1DQ/wOWWen8Nr8+WYzbGZqbeao\n8ujj8ThuueUWbN68Gb29vXjmmWewb9++nM+NHdJydjpAhH33buDDD8kNWL0acBxYAE25JpUxw7DX\nvheXdV2GF/a9gIHpAQzZJ2FScQt9WxvxHKKHow7D7uGcOHooFoI77IZGZkY0Kmw05jL4Vn0rOUHr\nMMlkkhQ1gzanUBIXej2giOcP3UTjUTgCDqhlpoI77wCgusIMqzt3Ds+cDdCqT9fizWfwaoUOg+PC\n9jTs3Uv68NxzgRdfTBt7MpnEuG8cly65FJ+MfQKAZDjt3k0e+FdeAa69FnjtNUG/JuXVMRk3AFCj\nqoEjQFaNOzuJtyoETqFnZd206Fsw6h494gYvZDEWEJdPzifGAKmdJDQEFo+T8hG33EI2HTocJMTJ\nddIS0+8A+exNN5H+F0I0SgYfrkVeQJzQc20U4+JIh+ykoiSh/+STTzB//ny0t7dDqVTiiiuuwMsv\nv5zzuT17+Bc6V6wgZVafegq48kqSx/7JJ8RDY1eIdIVccIfdOK3+Alw673q8MfAGRqam0KA3cbZb\nXk6miUwWwmtXEhVhpxxavVbUa+phm5CjoYF/oxQbLoNv0bXA4rGk2g7FQpBDDr06N2WRC50OkBU4\nN3bnxE60ajthYA51LUCtqg52f65LZvVaUaWoSgmZz0ce5MbGnI8CADoM87HbSnYY9/XlF7tPPwXW\nrQM2bAD+53+I8C9eTBamZZBhWd2yVIiLMfgXXiBe3fnnA9u387cdj6e9xf37iVGzhd6kMsHutyOZ\nTGL+/LTXWAiumRzbo2/SNmHMO3bEDV5o6Ka2VvhsJt8Av3QpCcMlEiTElk/0+/vJAPPjHwPPPENy\n+Rcv5ranZcvIc7F3Lxlkf/YzYu/5+OILEgY8dIgMQHzOE3u2UAiu/QNczFTWzVEVuhkbG0MLawm8\nubkZYxyVlTZt2oiPP96IjRs3oqenJ+O9FSuADz4A/vpX4JpriIcxMJBbCrh/qh+LTIuw4Xo5Xvjj\nYnzQewiD7kNYwxdkBJkdMPFiY5URmnJNhtc8HZyGqcokeHQHuDtJXa5GpaISjiDxKCcDkzBW1Arq\ndODwwxHJL/Tvj7yPFYZTBU3fAaBBa8Z0ONe1O+Q8hM7qtHUzosm3f+DkzhXoM92Pf/m/p7BkCfCt\nb/H/zqEh0tYllxDRf+wx4JRTyOJvg7YhNSACpN937QL+/GfguutImls+b+yBB0h//td/kT5dsyZT\n6FVKFaKJKJ764imsWpXu93xEIqQva1iTwkg8gnAsnDqfgClNMRNZNzMRoxcq9NEoEU4+oTcYiBBu\n3QqsXEnuPd9CJ9Pv8+YRgb/1VhKG5UKvJwund9wBXH01CcNMTfG3vXUreVa++13yTK1Zw/83Ce13\nQFzoZrYG+J6eHmzcuDH1n5SUJPQyIS4wgPPO24jf/pZcfHd3d8Z7Z58NvP8+8QRbW9PTr2yhH/eN\no17diM2bgeu/0Yjn3xiHU9GL89d28f7e004D3mRVAqhR1WAqkN4x6gymd8UKGd0B/o5nx+kn/ZPQ\nK8UJfSKYf8PUTttOtFecIMjYAaDZWAd33J6zaWrcN44GTfoJ/+gjcu/56GpsR8K8A3d/9FP86bEo\ntm0jhsnF+DiZGVRVAbfdRoo8XXghic/Xa+rRqm/FpH8S08Fp1NWRrIft20lBKabf+RZRX3oJePhh\n4Oc/J7OFs88+LPSV6fjAD9f9EDafDaefDmzZUvgeTUwQ0cnIoT+8OM882yqlCoFoQLDBCzldikFo\njD4aJf/lO2Seoa5OmNDv2gV0dOS/zksuAc45B7jqKuLhZ/loKdii+dOfEu//hhv42/3mN0m45oYb\nCi+ivvQS6fOeHvJMnXUWf7snnkhmIUKqlwoVejGHzpQq9N3d3XNT6JuamjA6mo5Nj46OopkjgPbS\nS/xtqNUklv7Xw2dlsD16JuYKAHa/HcqwGZ2dwFWXNCC08CkkzDuwuplf6L/1LfK7b7/9cFnbbKEP\nkRx6oQuxQB6h17Wk4vSTgUlo5cKFXq8HEiFt3rIPE74JVEWbhHv0tZUoS1bmFIdjvGuATMuffZYs\novHRrCP9aaqWo/30d3HiicSz4sJqTRvPz39+ODOmKj24VCgqcE7HOXilnwRlt2whBl5RkT63lEuk\nolES5rn2WuDee0lcd/Vq0n+MRw+QYnjusBvr1hHB3bAh/3mqXP3OzrgB0kIvdDHW7yd/D1+RLDZC\nQzdOJxEcIX6VUI/+mWfy9zsA/Mu/AH/8I/Bv/0acJr7MKLZoXnQRCQdm701gc889xMaZz+SLrW/f\nTsT9sceAM84gswA+1Grg0kuBK67IzADKJpEg9yi7LDcXYtMrpZ7JSUVJQr927VocOHAAQ0NDiEQi\neO6553DJJZeIbqexMV3DmVlE1ZdnevQ2nw2h6TqceCKwvjntgmor+F2SxkYiJkNDwA9+wO3RV1dV\nY2qKe7cdF3wdz86lt/lsUKNOlEef9GdeWzYTvgnIA2bOBS4u6uqA8njuSVhMxk1fH9DVRTyqfF3G\nCP2GL30Lj3z2CBYsDqO/n/uzfF4Su3bMN5Z8A8/vex4A8SbZn2cG+Wz27CFTfK0W+N73gN/+loje\ndHA649AYfSU5uF0uJ4be2Ah87WvgLeHA1e/s+DxA9hIEo0HBi7FCvTpAuME7ndwLm1zU1uZfjPV6\niZf+/PPAD3+Yvy29HrjxRjJwLVkC0f3Oh0JBZhMMfEIfi5F1mRNOIGL/7LOFhfT3vyd9fuWV/JU8\nXS6iN0ITJWZqZ2wpNX/EUpLQKxQK/P73v8e5556Lrq4ufOtb38KSfEO5AMrLyUiriJgwzTr31ea3\nwW8zY+VKIu4bz9iI763Nf8oTkI7rvvoqYKzg8OirjJieRk7lRj74pnLsXPoh1xD0yXZRBh/31OXk\n+e937Metm0mRcZvPhqS3XpTByyK5mTzukBv6Cj3++Z+B668nhZ24tsAzLK5ZjNg/x/DTU36KCd8E\nBlt/hX193E8on8F7w17oK4mFXrjwQrx16C3EE7nqy5cDztQhyoYdowdI1VN3mFhlczPwq18RkeIr\n9DU9nSug7IwbAKhSVpEzhXVJQQYvRuiFhm64rpOPQh79I4+QZ/jgQf7sFS4WLeKvo2O18i/mC4Gv\n3wcHicMiNFwJEK/+5puJ2L/4IvdnxNxPrZbMUITk+gvte6ZyaTgs7BqkoOQ8+vPPPx/9/f04ePAg\nbr/9dimuCWYzIAtleqN2vx3BqfQpVXd234n/vPA/BbVnMJBFopjHxBm6cTiEdzzfEXDsGP2gaxDa\nWIfgB1SvB6LuWtj9ma7Yi/texIMfP4i/7PwLHEEHIq5aUQafDOfWuPdGvFAptNi8mSxwCQkHlMnL\nUKOqwZOXPomdiSfw8fh7OZ9hCmRxTYc9EQ905cQCDJUG1KprU3sh2PDlbo+NcYtSttDrK/QZh83I\nZCSv/13uDcKcA7wjmN4VCwAKuQIKuQJaQ0RQ7FesRy9U6I3Gwp8DyMLy9DS/ML3yCvCd7wgLLbFZ\ntIgs3HO1K9ajz0Zsvwvh7LPz97tQOyorI/1UaDYnZm0GmP3wzawdPCKG+nog7suM0dv8Nvgm6or2\nHFatAqLTTeibSrslTJ0bsR4T12IkO0Zv9VqhCDaKMvigS4dIPJLaJPbWobfws7d+hhtW34DrX74e\nsUQMbqdCsMGbTEAiqMlZ4PWGvZie0KCtTfjfzNCqb8U3Fl6FgcRbOe/Z7aQ9rumwJ+xJZbEAwELT\nQhxwHMj5HF9BsrExklaXTY7QHw7dsFm1Kp23nfN9Po++KlP9VUoVVPqAoOJu2UKf79QwocYu5vlU\nKEhYgqvdeJysr5x8srC22Oh0RMS4yhdIIfRcu3D5+l0IK1eStEwuxNxPgAyehfre7yfrUUL2uACz\nX9hszgp9xJ3r0U+Pmovu+CVLAIPlW3h+3/OpTBQmxium42tqeIRenw7duEIuxP1GcZ6dW4ZaVW0q\nfNMz3AMAuH7l9bh6OVmBEnOd1dVALJAbuvFFfJie0OZdLMvHypZO+BS5pYsZY7+r5y786/v/ir32\n9DbXbKGvVdWmUlHZiDX4fKEbhiVL+EMOXDO5qeAUp9ArVQF4vYVr0rNTKwemB1DxqwreIy2Fnkkq\nJkYPkM9yzT5GRsjzK9TrzKa5OVfok0nhm4/44BvgSwkJLVpEQj9c/eVwCA/VAvw2z0bMTA6Y/U1T\nc1LozWYgOJ27GDs9Wlf0A9XWBjiHWqFSqnBgmniTTOhGCqFv1jXD5rMhHAvDHXYj5jWIjtXWqetS\nB5loy4k1Lqtbhq90fgUyyERdp9EIRH1aeMO5oZupMW3B8qx8NOrNUBpsOXFgRug3bt2I29+6Hfe9\nf1/qvWyhz06dZRBj8MFoEMlkkhReO4y+Itejb2vjLwnAdT/ZWUkMKqUKoXgARmPh1D12+QPm3N73\nRnJDXQBZG1EqCxfiEhO6AYiIcV3ngQOFy/Lmg6sgmcNB4uKVlcW3OxMevVJJ4vtcB6YX49EXymQS\nmnHDQEM3IB3vtad3xoZjYfgiPhgrqwWtlHPBGPxFCy/CC/teAJD2CMUsxvIJfXlZOVr1rRhwDsAV\nciHs0YuO1daq03F6R9CB+86+D8YqI65beR0SdybgdAo3eIUCUCQ1mHTnhm4mRjRFG7xZbUaZzpZj\n8FYrUN5KSky8fMXLGR77oHMwQzz5hF6MwTML6ey9HIZKQ4733NBAPGKuY/HYBp9IJjDqHk3l/LMx\nVBrgCrkEGTzbs2PChExBNy6EGLxYYeLz6GdC6EsN2wDkej0esoGNTamLvHx1f4oReqk9ehq6AfHs\nXBPEuBLJBOx+OwzKWjQ3FX+5jNCf2nJqquaNzWdDTZVZ8E5GgEx7w2Fu4VjbuBZP7n4Sdr8dIZdw\nj54xdrPajAkfUTq73446deZROmIfUDVqYXGmlSmZTGLCNwHrwdqiDb5OXYekys5p8E7zSzip+SRS\nJO7w32Hz2WDxWHBCwwmpzxorjZxnAnN59IkEd2ggO2wDkPr57rA7Y5NYWRkZJFjbPdJtsAb4/+v9\nP7T+rjVnQxlAdlVPB6dFG3yfow/rmtbxngsACDP4YoSeK6ZcqtBzlRWXQujlcu6DQ0rx6IF0ff5s\nxCRfAPzrcmxo6KYI6usB+4QCmnINXCEXbH4btHJzSaN7TQ0R6OaqReh39CORTGAyMAlllGxsErqI\nIpPxL87cfebdqZCF310pePDQ6ciK/TzDPBxykWpcUgi9XtaMYWc6mZgRX+v+ekElmbkwVhkRV7o4\nDT6qGsGGVRtQr6nHzomd2HRgE3aM78DqhtUok6dvMJ9Hzxg7O7NjcpKIYUVW2SAuoVeWKVGlqMrJ\nNOIL37DvpzNIBp6+qT606DNPtmAGJtFCP9WH8+afh0EXf+1cIQYvNnSTL0YvpDQvH1xnCDC7oUuF\nqyxyqUKfz6OfCzF6Gro5nG61wrwC2yzbYPPZUBkvfiEWIALd2grA04xx7zimg9Pk9CB3hejsE74p\nfGd1Jy5ccCG2XLtFcKlagIRZKiqAFvV8DEyTnSPZQh8Ok8FAzLWalM2p2jIAqXOzoHoBHFMyznM3\nhaAp1yAmC8JizVzlsloBr+IQ2gxtaNQSy7/g6Qvwp8//hK90fCXjs3xCX1FBMkbYIiV0IZaBK/OG\nT+jZnh1bjNnrCcz1OoPihD6ZTGLf5D5csOAC9Az14OvPfZ3z80IMXszObYA/Rm+ziWsnG67QDXs3\ndClkh+2SydJnC0IGeCHQ0M0MwYzuJ7WchF0Tu2Dz26AIlSb0AHmYmEXe/Y79mGeYJ7rTgfy7D1+7\n6jWc03FOUSN8nbITA05uoZ+YIPcl3+Hl2dRVNsMWTAu9I+iATmmCRpN/k1Q+5DI5quQ6DE9kPqUW\nuw+Doc+wvnk9ZDIZ/uvC/wIAvLDvBXx18VczPssn9EBuTjVfnJZP6LkybxoackNC8TgR2EpNCNss\n21L3nQt26KZQCWBmUe79kfeRRBInNp4IANgywF18R4hHLzZWzefR22zcB2sLZaZi9EBuv09NkUG/\n0FF/+eDqd0C80BfabQzQ0E1RaLVk+q4rq8VUcOrwrtDSQjcAecidU+WoUFTgjYE3cHLLyaKncQCZ\nGXDFfNkU0/HVsk4cnD6Iz8c/h91vzzgcpJiFqSZtMxzRdPlkR8CBqqRJUI2PfGiUeoxOZnrNFv8h\nNGlbU97wJYvSdRXYte+BtHBykT2FF+3RZ22aArjjv243ec6e630KJ/35JPRN9eGvl/0VYz/OTdOo\nrqyGM+QUdEwh0+9bDm3BNSuugUwmw0vfegkntZwEgOyxiCXSs6FCnl0sRoRJjEDnE/pS+n4mhV5o\nv4uB78BwsTH6lpbC/U6zbopAJiMjvCJSg0n/JPZN7UPU0VpyxzOHMvgiPty19S4sr1telEff3p6u\nc89FMkmMV0y+sl4PKMN1kMvkeG7vc7h6+dWoUqbdmWJioQ3VOshQlvJwHUEHlDFTSV4dQLxmqyu9\nSJFIAI6oBe3GdGy7UduIfzr5nwCQ9EQ2TCiEi+wpvNXKL/TGytzANZMhw4bL4JkBvs9BMmP22Pfg\n7I6zU2EnNszAVKjfgbTQTwensdhEDmFoM7TB5iOuZdNvm3DbG7elPl/Is7PZyHWK2cnKtRgbCJDi\ncGKcj2xqa4k4sbNjpIrRc/W7FI6dFB490+/59jvQ0E2R1NcDsmAtrF4rXu5/Gdh7meQj/KKaRXA4\nxC10AYWFPhgki7vZC4j5IAuyMly44EI88tkjaDe0Z7xfTCy0uhpQx9NxekfQAXm4dI9+XdM6jCnS\n+8unpoCKWgtaDZn71X911q/gvT239HKNqgbeiJezLHNDQ+aC39gYt8FnV65k0Ffqc0I3XAbPLHAO\nOgfRpm+DvkLPOXAAhwemkFPQeazM2gz7+tr0bRhyDeH9kfdT185QyLMrRvC4PHrGmxdYWZwTuZy0\nwe4fqWL0XP0+Ex59IkHutxibNxjI385V+oSBhm6KpKEBUHjb8dbgW5hfPR+2Q9KEbux2YM/3yEaW\ndU3rigrdFDJ4oed7smEMflndMrhCLnTVZpZfLtbgK8Jpobd6rVAEmkoX+raV8FccSB3TOD4OqOvH\nc7zh8rJyaMo1Od9XyBVYaV6ZOj+WTbZnxzZ4dtrkwPRAqrImG6EePbM70uKx4KY1N2FJ7RLe8xWM\nlUY4g85Uul6+AlfMFJ4dWjJWGZFEEqc9dhpObzs9Y4Ar5NkV4zFzLcaWGrZhYIdvpFgwZZgJj16r\nJWsxfn/6NbebxP7F1vop5NwJPeqRgQr9YRoagLJpInZyKBAIiBfkbBjPbmndUjh/6kSVsqoooS90\n2rzYeB2QNvgz28/EKS2n4KKFF2W8Pzoq3sOprgYU/rTQj7pHkXA1l2zwtRoTKgyOlJc8Pg5UVE/k\nbDTKx7rmdfh4LLfAebZnxxj8n3f8GY2/bUQymUQ8EceHox/itNbTcr7PFaPnOkeVmb6PekZx3crr\n8NZ1ufV7GJjQTVUVGcCzUwzZMIN8dgllpZzs9PuX0/8lY8ZRyOBHRorr92yht9ulF3q3m+xAFXi6\nZV5mwqOXyXIPYhEbn2eQ2uZpjP4wZMVchg2rNuArjVcIPtM1H2zPzlBJXO5iOr6lhXQS33Z4saM7\nkDb4ExpPwPs3vI/yssy0mP5+/nN3+aiuBhKeBli9xDItHgsiU6ULvanKBKXekTL48XFAppvIWDwu\nxCrzqlSJAAD4+ds/xx8+/QOvwb/c/zImfBOw++0Ydg+juqo6Q0gZDJUGuMKZHj1T0ZFdn2d6GjCY\nopj0T6JR25izjsCGCd0ApIY/X7GseJyUtNVqSdkHfUX6IXji60/gL1/7S86Mo5DBF9PvTIVV9sxj\nJjx6qeLzALH3iYl0HFwKoQdyZ3PFOHZA/n4HiM2LmcXTGP1h6uvJg/ToVx/FubofSdLp+Tw7MZSV\nkWPLPviA+/1SQjdcxOOkMFcxQh9xm+AIOpBMJmHxWBCYKD10Y1KZIFOlhd5qBeKV4jz6WnW6sFk0\nHsW9792LWzffmjGFZ/YO1NSQGv+VikpsObQFnQ91ZpQSZsPl0SsUh8MprIHZ4QCU1VbUqeugkOef\nxxsr01lC69cD73GXroHXS7zbsjJSaoJ9KM6FCy/EdSuvg6HSgEHnIKJxEvcqZPB79uQ/rYkLhYJc\nB7vdUlMrGZqakNHvUoRtABJOkcnIPWTalmIQ4RL6Yjz6k07i73egOI+eCj0yp3J8mRdiyZd9IZZv\nfhN48EHuUgilhG642LWLVA4Uu2hsMgEhRw0cAQecISfKy8rhGNeWLPTVVdWIV2R69CGlOKE3VZng\nCDjw5O4n0f5gOwAgHA+j1hzL6Pf6ekAmS2LQNYjLui7DtS9eCwD40yV/4myXK0YPcBu8XD+GJl3h\nB5eLWjoAACAASURBVEtTrkEkHkEkHsFllwGPP86dzcEe4L0Rb6owHZs2QxvaDG14sY+cipHP4CMR\nUlb4S18qeIk5ZMfppfTomUJhUsXnGdg2L6VHz+6rYkM355xDTrv65BPu94sN3fBl8tx5p/hrzMec\nFnrGs+PLvBCLRpO7OFNsx994IxHeU07JNXqx0ziA3+BdLuD++4GvfjX3vUIYjYB/ipyqZfFYSIVN\nCTw7U5UJ4TJHyuCt40l4kxMwa4QriUllwnbrdlz74rWweq144utPoFnXjKBiDIEAyVxijN3qtUKl\nVOFfz/5XXL38avhu92FNwxrOdrmyboBcg5+eBhQaN2+mDRuZTJZakF2zBviHfwDWrMk9O5cJ2SWT\nSfgiPt6F6KuXX53KwOGbyYXDwG9+A6xdK36AB3Lj9DMVupFa6CcmyADnckkzA8mexRfr2KlU5Nza\nCy8Enn46932xNs9XuTSRADZtAh5+WPw15mPOCj0TugHI2Y9SjO7M4owUUzmlkhxofsIJwEMPZb5X\njEfPZfADA6QIVSIB/OxnxV1jZcIEu8+RIfSlGjyJZycxMh4AAIzYPFDIFZzCxgcTerl1PTkq8Ypl\nV6DD2IEB58HULkmm3/fY92Bp7VI06Zrw5KVPQl3Ov/rH59FnG7zDAZSpPDnlDvK1y8Tpf/5z4K67\ngJ/+NPMzTL/7o35UKioz6vuwaTe0Y8xLRkmumVwoRJ6rzZuB/xR2iFoOMyX02aEbqWL0QNrmrVbx\nu8D5kMreAXK28ptvkrN22ccAhsNkY5vYXbxczt111wE//jHwl78Ud418zFmhr6sjnRKLAYcOZR4m\nXArsConxOLnRYr1vBpmMdEz2ifNSefQPPUS8x+eeEz9wMBjKazDpJx59XWUzlErinZSCTCaDXmnC\nyCSJsQ9N2mBWiyuiYqg04OlLn8bPTvkZfLf7oJAr0N3ejRf7XkwZPNPvo55RzDPOE9QuV4we4Pbo\ny6q8goVeW5F5LONVVwEff0w2IjEwQu8Nc4dtGBq1jRjzEKHn6vcXXiACunUrd3z+g5EP8MCHD+S9\n3tnw6AcHgXnCukUQTOhmYIAcGC4FUoVuGFauJDvjt29Pv8b0u9hkkWzn7uBBYMsW4PPPycxBSooW\n+v/93//F0qVLUVZWhh18py+XQFkZ2YnHdPz8+dK0yxZ6lwuiKldyceKJZKGUbaxSxei3bAG+zl0L\nSzA1KhMsviG8M/QO9LLSM24YqqtMGHM64PcDnsQEmvTiG75y+ZUwa8wpD/07a76DJ3Y/gYbGBMbG\n0gZv89lyKnnywRe6ya6lMj0NJMuFe/Sack2G0KtUwPLlROwZmAHeG8lciM2mUduYyoTSanNPmSrU\n7/e8dw/+acs/pY6d5CJ7d6xUQm8wEA/W7ycDsZRCzwwiUgo9V7+XmqZ92mmZC7PF2DuQa/Nvvgmc\nd15ph7jwUbTQL1++HC+++CJOP/10Ka8ng44OMspJ6dGzszpKmcYxVFSQeC3b4IsN3bA73W4nD/3K\nlaVdX62eCNmze56FKiad0Ju1ROiHhoCadnELsXw065pRpaiCef44BgZI38+fT84LFpq6mS90w96Q\n43AAcYVwodeW5x60ftppmZlXQj36GlVNKuNIqSTPEHvdaOtWIJ9ZVSqIEvz3jv/m/Qx7MTYcJmmf\nxcT6s2GqwA4PS+/Rd3QQkZda6Nn9LoXNc/V7MVGBbJvfuhU444zSro2PooV+8eLFWLhwoZTXksP8\n+eSP1+vJQqoUsEd4KTodIJ5db2/652JDN+xp3LZtJJWvlNkGAJiq0/NJZbBFOqHXmaDUOfDRR4Ch\nWRqhB4B5xnnQtgziwAGWRy9C6NVKNcKxcCp9kYHd70zILir35BVkNtkePcDf74U8erVSjWg8inCM\nBHrZnp3NRtrp6uL9OgacA/jxST/GB6M8+b3IDN1MTpLZsRQxbwBYuBD46CMySBUb9uRi/nxk9LsU\ncA3wpXr0K1Zk9rvTWZxHn23zH30EnHpqadfGh8iNwMWxcePG1L+7u7vR3d0t6HsrV5LV50WLpLsW\nszndSVJ0OkDy29kdPzkpPmMgexq3dy95oEqluhq4XPN7/NV3C+CWzqM3qUyoa3Pg5ZeBqiUSCr1h\nHtSGQbzwu1PhcpG0Uts7NsEZPTKZDCaVCVOBKXjCHmy3bsfyuuWor1+ZMnimcqU77MKyyqWC2uUS\n+sWLgX//9/TPk5PEwy3k0ctkMhiryGEm9Zr6lME3NpJ+X7aMP94bS8RwwHEAv+z+JX6//fe8v6O6\nmqQDAtKFbRgWLwZeekm6WTbDkiVk9u52A7/4hTRtmkwkNz8SIZkuUjh38+YdTikOkTBLMfYOZNq8\nzweMj/fgqad6St4YykVeof/yl7+MCY5DPO+9915cfPHFgn8JW+jF0N1NVqCvvbaor3NSXw+8/Tb5\nt1Qe/eLFZAGNoZgURq2WPJDJJDHy3l7g7LNLv7bqaqAxcDO+9+2lePuxLklS1gCSYlnf4cBr9wGn\ndU+gXnOyJO12GDuQSBzC4CBw8slkRiPGowdICGjYPYyT/kxKA5/Scgqe/cr7KaFnBnjm3FkhaMo1\nOUXYFi0iO1cTCeIt2+3AunWFPXogXT+HEXrG4Ht783vzg85B1Gvq0axrhiPAcczZYdgevdRCv349\n8MADwM03S9cmQERz3jwy2C1fLk2bcnm6nnxzszQ2r1CQ6zxwgFxnseUl2P3e1wd0dXXjrru6U+/f\nddddpV0oi7xCv2UL92EJs8Xq1cBTTwEixpSCsBdjpRT6vr70z3a7eKEvKztc3vbwYlFvL/D975d+\nbdXVwNiYDN3t3XjKSvKypcBUZULncgsci4EKk7Qe/fuj7+ONN9LTd7vfLipHv1nXnMpTX1q7lBzi\nwjqmkKlc6Qw6BeXRA9wevU5HQhcWC4lbM/0+UMCjBzIPXzGZ0icYFRL6vZN70VXblffwFqZNZjFW\nylIFAFkwXLqU7CWRmmefJaGrUkOWbJg4fWOj+MqVfDA2zwh9MQ5UdbXwfi8VSaJ2yXyFmkvkqqvE\n1XUvBHtxhjm1qVSamog37nKRRbVksrhCT8yuw0SiuJIHXLA9Oynznk0qE5KVDuzbB0xHxHnc+aiu\nqoYr5MKXv0xCA76ID8FokLMkMR9t+ja8uv9VrKpfhU1Xb4Ldb0d5eXogZY7mE+PRa8u18EV9Oa+z\nB3lmJifIo69KH5DOTlksZPAfWT7CiY0nwqQypRZ0uWD3u1QbDhnUalKaYQ33nrWSWLZM+jg1E6e3\n28l9kWIQYfd7sULP3pPQ2yu+1IUYihb6F198ES0tLdi2bRsuvPBCnH/++VJe14zBFnqLhUznSkUu\nJ9P4vr50pxcTZ2tqIkY5PEweyFIOiWBge3ZSbSsHDpcwOCw0Ez7pPPrsEMmuiV1YWrcUcpnwR3VJ\nzRK8O/wuvrroq2jWNSMQDSAcC6f6nrkPpXr0ADH4ffvIv5m+5yt/wIbtkTc3k2cRIG3lM/idEztx\nYtOJ0JZrEYqFEIlHOD83UwP80QizEC/l88/V72Jpasrs9znp0X/961/H6OgogsEgJiYmsGnTJimv\na8bQaIjH7fNJ2/FLlqSFvthZAiP0e/dK1+nsfGqpPfrp4DQSyUTO+balkL0xacA5gIUmcdldTC3/\nleaVkMlkqFXXYjIwmSv0ImP0fELf10cyeaanSQG2QouxQDpGD6T7fWqKLPDl66NJ/yTMajNkMlne\nk7rYFSylfM6PRrL7XQqWLJFG6JkyIlLaPBdzdmfsTMEcUyh1x3d1kelXsZ0OEM9ubAzYvVuajBum\nzdFRaeuHAMQjdQQccAQc0FXoUKEQcZxWHrIF1RFw8Faq5GNJLXGJO6tJkL9OXUfi/Oa0Z2dujCIY\nDQou28An9EuXkn53OEi8XqEgJYoLhW7YpY8Zg2f6Pd9scCowlbof1VXVvOEbpZLM5iYmqEc/U0Lf\n308G+GLrRzH97vWSdRSpNoVycdwJPUBu8OiodKEbIG3wY2PFF3piOn7XrtI3SjE0N5PBZ2hIuvoh\nQDp0I2XYBuAQ+qADpipxObC1qlo0ahvRYST5f4zQNzWRgzwsFsBY74a+Ui84JMQn9F1dxBtj97uQ\n0A279LGYfs8W+nwLsh0dJF1RquqvRyuMvUsp9BrN4YX3gfSaj1hqakgJje3byXMk9tQrMRyXQj9/\nPqknIZNJEwcH0gZ/4EDxI3NrK9ltuGuXdB69QkEOSnnvPekGNYAsJrpDbox5x+ac0MtkMoz9eCzl\nrdeqamHz2dDZSQxzbAxQm4TH5wESUuI649ZsJuGRDz4gBeiA3Fr0XLA9eman6c6d+YU+EA0giWTq\nkBRTlamg0Pf2khCOVDO5oxF2v0s54HV1kTpXRmNxyRcyGbHJV16RzrHj47gU+gULgNdfJ+dASkVH\nB4nRvvsu2TlYDKtXk3oXVqs0GTfsa9u0Sdo2FXIFtBVa9E/1S5ZxAxxejI14U5lcjoADJlVpu9pa\n9a0Ydg9jwQIifCMjgMokPD4PkKwbbyRX6GUy0m9PPJHe2OeNFC6WxhxPCJAQi8FA6tznqz3PePPM\n2bZM+IyPzk4iRB0dM+stznU6O8nMpq9P2k1e2f1eDGvWkHMtTjxRuuvi4rgU+rVrgbfekjY9rKyM\n1MD49NPi08MaGoh3d/75ZBefVHR0AM8/L/1ij6nKhE/HP0WLvkWyNisVlVAr1anYczEefTZLapag\nb6oPa9aQbeZmMxCWifPo2cKczTnnkAMpTjt8hK3QxVh2e+efT8IB+WZy7LANkF4Q56Ojg2zkm8lF\nvqMBrZasUWzbBqxaJV2755xDwi5MvxfDZZeR/xdz3oQYjkuhP+UU8v8rrpC23fvvJ3Wka8StHWbQ\n3w8884x01wSQHcYA8OUvS9turboWL/e9jPVN6yVtt83QhmHXMADpPHqLxwKjkQzu3/iGuIwbIH+Y\n5HvfI33/la+Qn4Xk0WdnzDz8MMm6EboQCwDVlfyLsUBagKTu96ORyy4j4REpNksxdHeTw2FK2dh4\n+eVk700xMX4xHJcTOrWaxFWlrimxZEnpmx5mokTpZZeRdQMpvRmA7GLdZtmGk1ukKX/A0G5ox5Br\nCCc0ngBHUHzWTTbqcjUCUVI4/tNPSb//13ZxHn2VkpwqEYgGcg4SNxiA225L/yzEo2cfZAKQGWGh\njTxTgamM2U11VTVGPaO8n+/oIB6n1P1+NHLffcC990rbpkxGSrSUSqnnQwjhuPToAelFfi6jUEhX\n+oANs9gppjyBEBihBw579CWGblRKVUromX4X69EDhRc/GYR49Nn7BYTgDDozdgi36ltxcPpg3u+s\nXXt8x+fZHE82n81xK/SU0nngKw/AdhvHKdkl0qZvw7B7GLsmdqFWXZvjQYuFLfQMzpAThgpxNXYL\nLX4CpLpkNB5FlSL/uXIqpQrBaBDxRFzw73eFXNBXpuvhntp6Kj6yfIREMiG4DcrxCR3rKUWjq9AJ\nPrhDDItMi/Cjv/8I+x37cd7881JZJsXCJfQ2nw1dNeJWKQstfgIkbKMp1xS8ZrlMnrquQt4/gzvs\nzpjd6Cv1UClVZLesxLMqyrEF9egpcw4m5v/3gb9jkan0wwjUSjX8UX/GaxaPRXS2UL6dqAxCwjYM\nfJuw+HCH3RkePQC06FryxunFYPVa8fiuxyVpizK3oEJPmXPoK/X4ySk/AQDRdW64qFJWIRgNZlRZ\nHfWMolknbgeZkNCNkIVYBrFC7wq5oK/IEnp9C0bd0gj9M188g2+/9G2EYiFJ2qPMHajQU+YkdSqy\nlVMKoZfL5KhQVKQELJlMwuKxiBZ6dsVOPoTUuWEQ7dGH3DBUZq4rNOuaJfPof/nuLwFAsoGDMneg\nQk+ZkzCCNs8gzenTugpdKr4+GZiEWqkWXNCMoVHbiDHvWN7PCKlzw6Ap1+SElPKRvRgLkL/rh5t/\nyHkguhg8YQ88YQ86jZ2weq0ltUWZe1Chp8xJrlh2Bfbfsh/KMqUk7a2uX41PrZ8CAEbcI2jVt4pu\no0VXOEwipM4NQzEx+myPfkE1KbCzZaC00+A+tnyMFeYVWNu4tuBgRjn6oFk3lDmJulyNBaYFkrW3\nrG4Z+h39AIBh13BxQq8vvPAp1qMXG7rJjtHfsPoGfDz2ccGQUj6C0SBufOVG/OL0X2DEPYK+qb7C\nX6IcVVCPnnJc0KRtSnmqI+4RtBnaRLdxpD16rtANcHjtoMAicT7eHX4X7YZ2fPeE7+KEhhPw2fhn\nRbc1m9y99W58Zj06rvVIQ4WeclzQpGvCmOew0HtG0KoT79HXqmvhi/hycvLZeMIewXsLxAh9NB5F\nJB6BWplbD7e6qhrTocI7dvmYDk6jUUtOJmkztMHisRTd1mzy9J6ncevfb53RM6v/f3vnHt3Udef7\nr2Rbb8mSbfkl2cY2BuMHxoRHwpSU4GWYkEAI0E7jtcJK09XJDKusNGnSJG0yTe4NBsJymmZWJ525\nKylcMhN6J5076coCBgiYJDzLMwTS4IAB+SG/ZD1tSZa15w+NhB5H0jk6R9hB+/OXdc7R72xvw/fs\n89u/h5AMugZh99in5N5U6CkZQam6NLTJmKqPXiwSoyy3DN2j3XGvsbgtyJOxa2SulChZC30whp4p\nEStPnoc3T7yJgjcK4PF5WNkLx+q2hnz/RcoiDLoGOdu40xBCYLKZ0Gvvxee3Pp/q4bBi4f9ZiKbf\npbnwfBx4Cf3zzz+POXPmoKmpCevWrYPNZhNqXBSKoES7blIRegBYWr4UR28ejXueS7VNVQ77FX24\nGEezqmYV3l3zLopURTjZc5KVvWjbQZdQobIQw2PD076sgtVtRZY4C2tr1+K46fhUDycpWf8rC7ds\nt3DDemNK3kB4Cf2KFStw+fJlXLx4EbNmzcLWrVuFGheFIiil6lL0O/rhJ/6UsmKDzC2ai8tDl+Hz\n+xjPc6mfz8V1w7QRG6RYVYwnm5/EwtKFuD56nZW9CNseW6juT05WDtQSNavibVOJyW5CmaYMTUVN\nuDR4aaqHkxCHxwGxSIybP72JAkUBzE7zHR8DL6FvbW2F+H+akC5evBg9Pd8O3x4l85BmS6GRajA8\nNgyr28qpRHE4Ro0R//Tnf0LO/87Bv136t5jznFb0HIQ+0Yo+SIGiAENjQ6zsRdsO3+TVyXW84/LT\njclmQlluGap0Vei2xnelTQe6LF2oLahFeW45SlQlU+IaEyy88r333sNjjz3GeO7VV18N/bxs2TIs\nC3bCoFDuIAaNATesNzAxOQFZdmqF/8s0t98Env2vZ9HW2BZxPrpmfCI4regZ6txEU6AowPDYMCt7\n4QyNDUGv0Ic+a2VaQYX+t6d/i7bGNs5loRPRY++BQW1Apa4ypbeYO8np3tO4p+QeAIGHaHgfgnA6\nOzvR2dmZljEkFfrW1laYzbGvGu3t7Vi9ejUAYMuWLZBIJGhra4u5DogUegplqjCoDfjL8F+gkWpS\nrog5r/h2F4/vlMf2jOTSKEUo100QvUKPqyNXWdkLx+w0RzR4F1Lou0a68JN9P4HNY8Mvlv5CEJtA\n4OFUpCpCkbIIQ64h+IkfYtH0iy157sBz6DjRgd+u+i2A2BaS4UQvgl977TXBxpFU6A8eTJxxt3Pn\nTuzduxeffPKJYIOiUNJBqbo0JPSpIs2Wov9n/Th26xjev/R+xLlJ/yRsbhvrlet0cd2kU+j7HH3I\nFmdjz5d7BBX6kfERlGnKAnsKUjVGx0d5t5wUknf+/A6KVEXoONEB4HYGs06ui2gheafg5brZv38/\nduzYgaNHj0KWjh54FIqAFCoL8Y3lG9419ItVxdDJdfjPv/wnvJNeSLICndytbivUUjWyxez+W00H\n1w0hhFHohRKjUfco7jPeh4sDFwWxF2R4bBjNxc0AAm8yQ2ND00roN+3dFPF5dkGg3DbbLmVCw+td\nZ/PmzXA6nWhtbUVzczM2bdqU/EsUyhShV+gFEXoAoTIHx24dCx3jEnEDcBf6ZB2xUhH64P3DC7yV\nqErQ7+znZCcelnELqnRV8E56ObdOTER4i0m9Uo8hF/c3mXRSqi4Nje/wxsOhcN4SVQn6nHe+aByv\nFX1XV5dQ46BQ0o5eqcfFgYtYP2c9b1v3lN6D2fmzIyo9com4Abi7bhoLGxNeo1fqOQt9cDUfvmdR\nnluO072nOdmJR7DPrUEdyEwOrmz5Mjw2HJrrMk0ZbtpuYimWCmKbL2+dfAt9jj54XvZA+roUdfrb\nncyMGiNO9nLPdeDL9Nu9oFDShF6hh5/4U06WCkcsEuPROY9GRHyksqJnW6aYzWasVqaFw+PAxOQE\n6zFEu20AYbtWDY8HopBm5s1El0W4hWH4pndNfo2gtvmy+4vd2LxoMyRZEpBfkYg2j+GlOO4kVOgp\nGUOlLlDbXqga93UFdbgyfCX0meuKPlgCgU2mJJvNWLFIzKrdYThMQl+kEq4Mwi3bLZTllmGOfo6g\nVTHDw1iXli/FH6/8UTDbfBkeG8bTi59mPKdXcH/rEgIq9JSMoUpXBSDgdhGCuUVz8cXAF6HPXFf0\nkiwJxCIxvJPepNey2YwFuLtvmIS+QFHAqxpmODetN1GRW4FyTblgxdImJicwNjEWmo+WyhZ8PfI1\nq3lMxsdXP8b8f56fcpkCQgj6Hf1xu5flK/Kp0FMo6SRbnI2jTxzFIsMiQezVFtTi+uj1UItCrkIP\nsPfTM7URZKJAUcBpY3LANYBiZazQCyVGN203UaGtQJGqSLDU/5HxEehkulDcfJY4C0XKIgw4B3jb\nPnrzKM6bz2P+v8xP6fs2jw3yHDmk2VLG8zqZDnaPHZP+ST7D5AwVekpGcX/F/YIl1kizpajX1+PT\nm58CCLyys02WCsJW6JkagzPBVaTNTnOEDxkAFDkKAEhYjpkNE5MTGHAOwKA2BITYxV+IgYCLLHqe\nw6uT8sFkM+GV+1/BBfOFlN4QhlyRWcbRZImzoJFq4mbHpgsq9BQKDzbUbcC+b/YB4O6jB9gJPSGE\nveuGow84Xt0fIVb1PfYeFKuKkZOVgyKVMCtuIDLiJohQQu/wOrDYsBjFquLUy0ko4ws9EHDfCOUa\nYwsVegqFB1W6Ktyy3QKQPtfNuG8c2eLsUGJWIrhmx8bb5BVC6C8NXsIc/RwAgSbmQjXdYCozUaIu\nEUTonV4nlBIl9Ap9ShvSbArm5cvzebV+TAUq9BQKDypyK0JCbxm3IE/OrulIEGVO8uYjdo+dldsG\n4C7Q8d4UhBD68/3nQ8W8NFINHF4HL3tBmArHlapKBUlEcnldUElUKFQWppSExabDGF3RUyjfMspz\ny0NC7/A4OGfdKnIUGPeNJ7yGS3tCzkIfJz6f66YuE2aXORR9EnxzEaLpRjp99E6vEyqJCkaNMaWy\nDTa3LenfqkBRQFf0FMq3iWDM+ee3PofD64goJcAGRY4i6aYnF6HXyrSwedh3eou3om8ubsa/X/l3\n1naYGHINhQQ5W5wNWbaMdYJYIoJJWOEIKfTKHCXWz1mPQ9cPcf6+3WNPupeSL7/zIZZU6CkUHgQj\neB7Z8wgcHgfUUjWn78tz5KyEnq1dtUQNh4edi2RicgJWt5XR3fRY42MptSUMZ3hsOCIChcvYEnEn\nVvR6pT6lCp52rx0aSeKHslBj5QIVegqFJ79Y+gusrF4Jn98Hebac03cVOQqMTwjnulFL1ax94S8f\neRkqiYpxk9egNsDtc/Ny30SHm6qlakE2ZEfGY6ObhBBPp9eJcd84lBJlyqWa2fytjBpjqH/xnYIK\nPYXCk8WGxTDZTVBJVJwbmgjtumEb3eLxefDGsTfiJmGJRCI0FDbg8tBlVvdlYmhsKELohSrRO+Ac\niIlVz1fkw+l1wuPzpGz346sfo7WqFZIsScpCb3MnD4M1qA2CZQmzhQo9hcKTfHk+Pr/1OWf/PMBO\n6Lls8rJ1j/Q5+lCeW46vf/J13GvqC+uxfNdynOw5yVmg/cQPy7glQugLlYWC1NDpdfTGlBgQi8Qo\nVhXzKq/8xcAXWFi6EEDqzVfYPJR1ch1sbvb7KEJAhZ5C4UlZbqCPbCqv44psBcZ8wq3o2bpueuw9\nMGqMCZuklKhKQEBw37v3Yd0f1rG6fxCb2wZljhI5WTmhY0IIvc/vw5BrKKY+D8DffXNt9Bpq8gOd\noGTZMhCQUHkLtrD5W6kkKsFCTdlChZ5C4Ul5bjn+dd2/4r0173H+riJHAZc3cSSK3WsPNTpJhjxb\njkn/ZFIXxhcDX2BOwZyE19xnvA9amRYffu9Dzi0Ko902gDBCP+QaglamjXiABOEr9HaPPcKVpZao\nk/5tmGwkE3q1RC1oExY28Go8QqFQArQ1tqX0vVxZblKfut1jh0FtYGVPJBIhT54Hy7gFJeqSuNed\n7T+Le433JrS1cuZKjL4wCsu4Bb1/4va2Mjw2HFMKoFBZGFG/PxUShS+WqPhlxzo8jogHarCMNJey\nFjZP8r4BKolKkOgjLlChp1CmkKAoJ4KL6wa4XQqXSeif/a9nIRKJMOAaQIkq/oMgHJ1MB++kFy6v\nC0qJktV3mAq8FSmLeIdsOrzx9yv4rugd3sjwWC4dwIKw+VsFK1uG9xtONym7bl555RU0NTVh3rx5\naGlpgckkTEcaCiWT0Ml0wgt9gloqvz75a7x54k3s7drLutKmSCQKdEbisAcRniwVRAjXjd0T3401\nQzsD31i+Sdm2wxOZ8JYuoQ/avpOr+pSF/uc//zkuXryICxcuYO3atXjttdeEHBeFkhHkyfOSlqy1\njFtY1aIPwrZxCBeXBNeQwOhkKSDQFIWv0CeKQLrXeC+vNwaHN9J1w1XofX4fPD5PqMxzItTSO+un\nT1no1erbE+J0OlFQwK0ON4VCYee6GXINoVBZyNpmvoJ5RR9dZ4ZLpU2jxsip1+mAayBmzFzLMzCR\naMVcnluOfmc//MTP2S4hJCazmavQB8fGJpfiTkfe8Iq6+eUvf4ny8nLs2rULL774olBjolAyE37X\n1wAAEXVJREFUBp084LpJVOxraCxxM4to4tVSsXlsoczdV+5/hVOlTYMm8Yre5rbhg0sfhD73Onpj\nNpBzpbm848ej/ejhZIuzochRpLRSPnrzKGbmzYzIbFZJVJwyeTmFwd7hyJuEm7Gtra0wm2Pbf7W3\nt2P16tXYsmULtmzZgm3btuGZZ57B73//e0Y7r776aujnZcuWYdmyZbwGTaHcLciyZcgSZWFsYoxx\no9Pn98HmtnES5Xx5Psyu2P+3g65BGDQGPDjzQfzDd/+BUxavQW3A1ZGrjOcIIdjy2RbsOL4DiwyL\nMOgahMlmiklqUklUcE244Cf+lLt8JRNTnUwHq9vKuYro2b6zaKlqiZgTrlUmuQg9k4++s7MTnZ2d\nrO/HhYRCf/DgQVZG2trasGrVqrjnw4WeQqFEEnTfMAn9sVvHUKevQ5Y4i7W9fEU+Y+mCQdcgCpWF\nePvBtzmP0agx4nD3YcZzB68fxI7jOwAAM/9xZuh4pa4y4roscVZoxc1ViINE+9Gj0cq0GB0fRXlu\nOSe7XZYuNBQ2RBwrVHDbPOaa2Ba9oo9eBAu575my66arqyv080cffYTm5mZBBkShZBpMfvpg0tOB\n6wewtnYtb3vAbaFPhaaiJhy9eZTR7uh45GZyMNomekUPBNw3QXdIv6Ofc336ZGKqlWlT6sdqspti\nHg5co4TY1LkJkkpEDx9SFvqXXnoJjY2NmDdvHjo7O9HR0SHkuCiUjIEp8mbzvs0of6scR7qP4K/K\n/oqTvXibnoOuQRQpixi+kZzqvGrML5mP072nY86ZnWY8MOMB7GjdgeNPHsfQ80Owvcjsi8+V5YYe\nDKVvluJ3Z37HaRzJ6v4Uq4rR7+Be72bAORBTVoGr0HP10d/JzdiUE6Y+/PBDIcdBoWQsTCvw46bj\nGHQNYtA1iOWVyznZi1eQi8+KHgg0I7lgvoC/nvnXEcf7nH1orWrFc0ueCx1LltRUoa0AAGzauwlP\nLXiKtc8+URw9AMzMm5lSLP2AayDmIZhOof/WrOgpFIowBCNvwvFOegEAZ//2LGNdl0TkSnPTIvQ1\neTW4Nnot5nivvRcGDbsSDQa1AUduHMGlgUtoLGxERW4F41tCOKUdpTjSfQRA4sxYAKjX1+NkL7dY\nej/xB952VHdO6O/0ip4KPYUyxUSv6J1eJ3rsPTi88TDml8znbE8r0zKGMZrsJpSqS1MeZ3VeNeNq\nuc/Rx9pupbYS249txwuHXsBi42I8Me8J/MdX/5HwO/3Ofuz9Zm/gZ0d/wofV2tq1+OT6J6EHJRv6\nHH3Ik+dBli2LOF6oLORUzI1NnZsgdEVPoWQYebJIoT9w7QDuNd6LByofSMmeRqqBw+uISRz6evhr\nzMqflfI4i1XFjB2nuAj9r5b9Ck82P4ljpmNYWr4Us/Nn46btZtzru0e7AQBHuo9g0j+JG9YbMdE8\n4SglSlRoK9A10hX3mnA+vfkpyn5dxvjwUElU8E56WZcqTuZWirb9rSiBQKFQhCF6M/ZU7yncX3F/\nyvayxFlQ5igjkn0IIei2dqNaV81rnExx5f3Ofk5vCn9T/zcAgFU1q1CWW4YD1w5g0j/JeO3rn70O\nIJBvsP3YdkizpUndI3X6OlwZupJ0HE6vE9/d+V0AwMtLX445LxKJOK28g20I2fCtKYFAoVCEIdpH\nf8F8AXOL5vKyGe2+GfeNI0uUBXkOt5624eTL8zEyNhIREjk2MQaPz8PaZQEE6txvbdmKAkUB6vX1\nsLqt+HPfnxmv1Ug16FjRgeeWPIeOEx2o1MZfzQep09fhq+Gvkl7XursVANDzTA++V/89xmuUOUrW\nNendPjekWVJW136rSiBQKBT+hPvofX4fTvacxJKyJbxsRkfeWN1WToXRmJBmSyHJkkSsRM1OM4pV\nxZyybNVSNV78TqBkik6uw0M1D8VtQj7kCpR/WFK2BJZxS0yNeyaai5txoudEwmv8xI9LA5fw0nde\nSvg2EszmZYPb547x88djWpVAoFAo6Sdc6HvsPdBINaxLCMcjV5YbI/Rsk3kSESyYFqw3Y3aaEzY4\nYUOBooCxNg9wu659obIQ7zz0Dqs8gNaqVjz+/x8HISTuA6jf0Q+VRIX2lvaEtoLNR9jg8XlCteaT\nkcglZHPbWD8w2EKFnkKZYsKFvs/Rx7qbVCLSsaIHbrtvZmhnAAgIJlP/Vi7olfq40S1mpzkU9vh3\nC/6Olb1cWS4kWRKMukfj1gi6PnodVbqqpLa4um5Yr+ilzE3cj3QfwfL/uxx/v+DvWdlhC3XdUChT\nTKGyEAPOAfiJn1NMeiKis2NtbpsgQh8dChp03fChQB5/RX/DeiP0UOFCiaoEZmdsYbcgX498jZl5\nM+OeD6KUKNPiuom3ot91cRfur7gf75x5h5UdtlChp1CmGEWOAlqZFv2Ofk6hiomITpqyuq2cNkzj\nEV3r3uwyo1jJf0XPJPRWtxWTZBI6mY6zzRJ14v6xp3pPYZFhUVI76dqMZUqYuma5hl0Xd2HX2l04\nvJG5gFyqUKGnUKYB1XnVuDJ0BX3OPpSq+At9ul03Qfod/YL46JlcN+f6z6GxsJHTRm+QmryauGWV\ngcBeCCvXDYcVvWfSw3pFr5QoMTYxFpHr8Ppnr6MitwIztDNSzqGIB/XRUyjTgEdmP4IV768AAOx8\nZCdve1qZFv3O28W9bB5hXDcxK3ohXDdxNmPP95/HQsPClGw2FDbgy8Ev4563e+ys3nCUOew3Y7m4\nbsQiMeTZcoxNjIX61Pbae/HOQ8K6bEL3S4tVCoXCiXnF80I/88leDZI2141ceKHXK5hdN8PjwylX\n25yZN5OxLk8QtnVplBKOrhuWUTdArJ/+6shVQf72TFChp1CmAcGMVcvPLbiv7D7e9vLkeREuFqFc\nN9GbsXwLpQH/47phiKO3jFs4ddYKp1JbGSqfwITNbWMl9Koc9nH0Hp+HtY8eCCSDBbOXxyfGYXaa\nQ1U9hYYKPYUyDajSVeHEj05AJ+e+8chETX6kj1ow102Uj55LxcZ45Mpy4ZpwYWJyIuI4H6GfoZ2B\nW7ZbcRuFc1rRsxR614SLdQkEIHIfpcvShSpdFbLF6fGmU6GnUKYBIpEI9xrvFcze7PzZuDZ6LVRD\nRrDN2DAfPSEkaWs/NohF4hiXEMBP6OU5cujkOsbIGz/xJ2wyHg7bqBtCSKDvb05qQn+m70xKlUrZ\nQoWeQrkLkWZLIc2ShnzAgmXGhq3o3T43csQ5nOvlM8Hkvhl0DfLKEK7UVuL66PWY418OfolqXTWr\n1TPbFf24bxySLAmn3r7B/rYA8NXwVzE9a4WECj2FcpcS7gMWckV/bfQargxdYb0qZgNTly2z04wS\nVeqhm/eU3oNjt47FHL9gvsA6modt1I3L6wpFz7AlfEU/PDYMvSJ5HZ9UoUJPodylqKW3k3KEyowN\nRu5s+H8b4PQ6OYtbPHRyXUSUkHfSC6vbymtFv8S4BOfM52KOD4+xj+ZJVIcnHKfXycltA0Q+3EbG\nRpCvyOf0fS7wFvqOjg6IxWJYLLHd4SmRdHZ2TvUQpg10Lm6TrrnQSDWheipChVcGXRPBxhl8/fNB\ntDItRt2jobkYdA1Cr9BzcoVEU6ouxYBzIOb48Ngw8uXsRLVYVZywlEKQVB56pepS9Dp6AQAj4yO8\nC9klgpfQm0wmHDx4EBUV6QkJutug4nYbOhe3SddcqCVq2D12ODwOiEViKHIUgth9Yt4TaCxqhMlu\n4h1DH0Qn02F0/LbQC5FxG0+kgxUx2VCiDtTMCa/Bz4RrgrvrxqgxwmQ3hcbE9uGTCryE/tlnn8Ub\nb7wh1FgoFIqABH30wfo5qZQSYGLVzFWwuW042XMy5czVaHQyXUSXLSESseIJPZfVs0qiCkXUJMLp\ndXIKrQSAMk0ZTLaA0AvxYEtEykL/0UcfwWg0Yu5cfp1wKBRKepihnYEuS5dghdKCaKQa/PGrP+Ld\n8+/ioZqHBLGpk8cKPZ+NWCAwzgn/REx45PDYMCd/ePheRzxcXhdnH31ZbhlMdhMcHgd8fp8grrV4\niEiCd5LW1laYzbFPxC1btqC9vR0HDhyARqNBZWUlzpw5g/z82MkTahVBoVAomUYylxFbEgp9PL78\n8ku0tLRAoQj4/Hp6emAwGHD69GkUFvJLh6ZQKBSKsKQk9NFUVlbi7NmzyMtLLYuNQqFQKOlDkDh6\n6p6hUCiU6YsgQn/9+nXG1fz+/ftRW1uLmpoabN++XYhbTVtMJhMeeOAB1NfXo6GhAW+//TYAwGKx\noLW1FbNmzcKKFStgtd5OCtm6dStqampQW1uLAwcOTNXQ08bk5CSam5uxevVqAJk7F1arFRs2bMCc\nOXNQV1eHU6dOZexcbN26FfX19WhsbERbWxs8Hk/GzMWTTz6JoqIiNDY2ho6l8rufPXsWjY2NqKmp\nwdNPP83u5iRN+Hw+Ul1dTbq7u4nX6yVNTU3kypUr6brdlNPf30/Onz9PCCHE4XCQWbNmkStXrpDn\nn3+ebN++nRBCyLZt28gLL7xACCHk8uXLpKmpiXi9XtLd3U2qq6vJ5OTklI0/HXR0dJC2tjayevVq\nQgjJ2LnYuHEjeffddwkhhExMTBCr1ZqRc9Hd3U0qKyuJ2+0mhBDy/e9/n+zcuTNj5uLTTz8l586d\nIw0NDaFjXH53v99PCCFk4cKF5NSpU4QQQh588EGyb9++pPdOm9AfP36crFy5MvR569atZOvWrem6\n3bTjkUceIQcPHiSzZ88mZrOZEBJ4GMyePZsQQkh7ezvZtm1b6PqVK1eSEydOTMlY04HJZCItLS3k\n8OHD5OGHHyaEkIycC6vVSiorK2OOZ+JcjIyMkFmzZhGLxUImJibIww8/TA4cOJBRc9Hd3R0h9Fx/\n976+PlJbWxs6/sEHH5Cnnnoq6X3TVuumt7cXZWVloc9GoxG9vb3put204saNGzh//jwWL16MgYEB\nFBUF6moUFRVhYCCQkt3X1wej0Rj6zt02P8888wx27NgBsfj2P7FMnIvu7m7o9Xr88Ic/xPz58/Hj\nH/8YLpcrI+ciLy8PP/vZz1BeXo7S0lJotVq0trZm5FwE4fq7Rx83GAys5iRtQp+pG7ROpxPr16/H\nb37zG6jVkXVARCJRwnm5W+bs448/RmFhIZqbm+PGAWfKXPh8Ppw7dw6bNm3CuXPnoFQqsW3btohr\nMmUurl27hrfeegs3btxAX18fnE4n3n///YhrMmUumEj2u/MhbUJvMBhgMplCn00mU8ST6G5kYmIC\n69evx+OPP461a9cCCDylg0ln/f39oTyD6PkJ5iLcDRw/fhx/+tOfUFlZicceewyHDx/G448/npFz\nYTQaYTQasXBhoFTAhg0bcO7cORQXF2fcXJw5cwZLlixBfn4+srOzsW7dOpw4cSIj5yIIl/8TRqMR\nBoMBPT09EcfZzEnahH7BggXo6urCjRs34PV68Yc//AFr1qxJ1+2mHEIIfvSjH6Gurg4//elPQ8fX\nrFmDXbt2AQB27doVegCsWbMGe/bsgdfrRXd3N7q6urBo0aIpGbvQtLe3w2Qyobu7G3v27MHy5cux\ne/fujJyL4uJilJWV4erVQFu/Q4cOob6+HqtXr864uaitrcXJkycxPj4OQggOHTqEurq6jJyLIFz/\nTxQXF0Oj0eDUqVMghGD37t2h7yREiA2GeOzdu5fMmjWLVFdXk/b29nTeasr57LPPiEgkIk1NTWTe\nvHlk3rx5ZN++fWRkZIS0tLSQmpoa0traSkZHR0Pf2bJlC6muriazZ88m+/fvn8LRp4/Ozs5Q1E2m\nzsWFCxfIggULyNy5c8mjjz5KrFZrxs7F9u3bSV1dHWloaCAbN24kXq83Y+biBz/4ASkpKSE5OTnE\naDSS9957L6Xf/cyZM6ShoYFUV1eTzZs3s7q3IJmxFAqFQpm+0A5TFAqFcpdDhZ5CoVDucqjQUygU\nyl0OFXoKhUK5y6FCT6FQKHc5VOgpFArlLue/AbZkJhWAIWk4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10686c190>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff_deterministic = np.diff(data_deterministic)\n",
      "diff_noisy = np.diff(data_noisy)\n",
      "\n",
      "plt.plot(diff_deterministic[100:200])\n",
      "plt.plot(diff_noisy[100:200])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1068b1210>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4+GKYNAnefht+9ztRCzDY2LkTJk4ElUp6bWw02hVeBvsG46/0Jyk0CX+lf7eu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\nwCfAemPqT8hjIWfPpsM4wGt7XuP9w72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rvrP/HacUUJnwgHBKakpoMjUR6BNIUhIsWAD/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1068b1190>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.ndimage import filters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 490
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "target = diff_noisy[4:]\n",
      "\n",
      "features = np.hstack([\n",
      "    diff_noisy[3:-1].reshape(-1, 1),\n",
      "    diff_noisy[2:-2].reshape(-1, 1),\n",
      "    diff_noisy[1:-3].reshape(-1, 1),\n",
      "    diff_noisy[0:-4].reshape(-1, 1),\n",
      "])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff_noisy[3:-1].reshape(-1, 1).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(995, 1)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(995, 4)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "target.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(995,)"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression\n",
      "from sklearn.cross_validation import cross_val_score"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores = cross_val_score(LinearRegression(), features, target, cv=5,\n",
      "                         scoring='mean_squared_error')\n",
      "np.mean(scores), np.std(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(-0.0056889846641182311, 0.00042333703644583146)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import ExtraTreesRegressor\n",
      "\n",
      "scores = cross_val_score(ExtraTreesRegressor(n_estimators=10), features, target, cv=5,\n",
      "                         scoring='mean_squared_error')\n",
      "np.mean(scores), np.std(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(-0.0064764457644039212, 0.0006396078877444469)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import GradientBoostingRegressor\n",
      "\n",
      "scores = cross_val_score(GradientBoostingRegressor(n_estimators=5, max_depth=3), features, target, cv=5,\n",
      "                         scoring='mean_squared_error')\n",
      "np.mean(scores), np.std(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(-0.0089533892147768551, 0.00085108739653868854)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVR\n",
      "\n",
      "scores = cross_val_score(SVR(gamma=0.01, C=.1), features, target, cv=5,\n",
      "                         scoring='mean_squared_error')\n",
      "np.mean(scores), np.std(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(-0.01069472452521234, 0.00088079677269642235)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}